Efficient Parallel Tree Reductions on Distributed Memory Environments

IPSJ Online Transactions

2014

Self Cite

Parallel tree contraction is a well established method of parallel tree processing. There are efficient and useful algorithms for binary trees, including the Shunt contraction algorithm and one based on the m-bridge decomposition method. However, for trees of unbounded degree, there are few practical tree contraction algorithms. The standard approach is "binarization," namely to translate the input tree to a full binary tree beforehand. To prevent the overhead introduced by binarization, we previously proposed the Rake-Shunt contraction algorithm (ICCS 2011), which is a generalization of the Shunt contraction algorithm to trees of unbounded degree. This paper further extends this result. The major contribution is to show that the Rake-Shunt contraction algorithm is a tree contraction algorithm that uses fewer types of primitive contraction operations if we assume the input tree has been binarized. This observation clarifies the connection between the Rake-Shunt contraction algorithm and those based on binarization. In particular, it enables us to translate a parallel program developed based on the Rake-Shunt contraction algorithm to one based on the m-bridge decomposition method. Thus, we can choose whether to use binarization according to the situation.

Section: Discussionmentioning

confidence: 99%

“…Kakehi et al [6] proposed a parallel tree contraction algorithm for rose trees, which is different from both the Shunt contraction algorithm and the m-bridge decomposition method. With their approach, it is assumed that the input is in an XML-like format.…”

Section: Related Workmentioning

confidence: 99%

Parallel Tree Contraction with Fewer Types of Primitive Contraction Operations and Its Application to Trees of Unbounded Degree

Morihata

IPSJ Online Transactions

2014

Self Cite

“…Kakehi et al [13] showed a parallel tree reduction algorithm from the nodes in chunks. Based on the idea given by Kakehi et al, Emoto and Imachi [6] developed a parallel tree reduction algorithm on Hadoop, and Matsuzaki and Miyazaki [17] developed a parallel tree accumulation algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Partial-tree-based Approach for XPath Query on Large XML Trees

Hao

Journal of Information Processing

2016

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XML is a popular data definition language and is widely used for representation of arbitrary data structures. For queries on XML documents, XPath has commonly been used in many applications. The complexity of applying queries increases as the number of nodes in an XML document increases. Querying very large XML documents becomes really difficult when there is not enough computer memory to store and manipulate the whole tree data. The objective of this study is to develop an algorithm for querying very large XML trees in a distributed-memory environment. We split a large XML document into small chunks and parse the chunks to create special trees called partial trees. Then the query is executed in parallel on the partial trees. The results from the partial trees are concatenated to form the final query results for output. The algorithms were tested on a 16-node PC cluster, and the experiment results showed a speedup of a factor of 6 on 16 nodes.

“…We can consider the text representation of XML as an flattened tree. Parallel algorithms for these flattened trees were proposed by Sevilgen et al [28] and by Kakehi et al [16]. We place the intermediate results on the stack in these algorithms and these results are communicated among c 2014 Information Processing Society of Japan processors.…”

Section: Parallel Processing On Tree Structuresmentioning

confidence: 99%

Dividing Huge XML Trees Using the m-bridge Technique over One-to-one Corresponding Binary Trees

Kawamura

IPSJ Online Transactions

2014

Self Cite

Tree data such as XML trees have recently been getting larger and larger. Parallel and distributed processing is a promising way of dealing with big data, but we need to divide the data in the first step. Since computation over trees often requires relationships between parents and children and/or among siblings, we should pay attention to such relationships. There is a technique called the "m-bridge" for dividing trees. We can easily compute m-bridges for trees of any shape. However, division with the m-bridge technique is sometimes unsatisfactory for shallow XML trees. We propose a method of tree division for XML trees in this study, in which we apply the m-bridge technique to a one-to-one corresponding binary tree. We implement the tree division algorithm using the Simple API for XML (SAX) Parser. An important feature of our algorithm is that we transform and divide XML trees in the order that the SAX parser reads the trees. We carried out experiments and discuss the properties of the tree division algorithm we propose. In addition, we discuss how we can use the divided trees with query examples.